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Machine learning for energy conversion prediction and photovoltaic-on grid protection system using IoT

10.11591/ijres.v15.i2.pp416-425
Habib Satria , Muhammad Fadlan Siregar , Indri Dayana , Dadan Ramdan , Hermansyah Hermansyah , Muhammad Irwanto , Syafii Syafii
The advancement of photovoltaic (PV) systems in tropical regions faces significant efficiency challenges due to fluctuating panel surface temperatures. This study addresses these issues by implementing machine learning (ML) models, specifically k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), to classify and monitor panel temperatures. To enhance system resilience, an internet of things (IoT) based on-grid protection system was developed, featuring a dual-relay redundancy mechanism that triggers an automated trip when the current exceeds 1.30 A. This integration ensures the protection of both the PV infrastructure and household electrical loads. Experimental results demonstrate that the KNN model exhibits superior reliability with a testing accuracy of 93% and a baseline performance of 96.67%, successfully identifying both normal (25 °C to 35 °C) and high-temperature (36 °C to 48 °C) states. In contrast, while the XGBoost model reached a maximum validation accuracy of 94.44% during training, it only achieved a testing accuracy of 84% and showed significant limitations in detecting normal temperature patterns. Beyond classification, the IoT framework proved highly precise in real-time energy monitoring, with sensor error rates below 2%. This research offers a strategic solution for optimizing energy conversion and system reliability, providing a robust framework for sustainable clean energy management in tropical climates.
Volume: 15
Issue: 2
Page: 416-425
Publish at: 2026-07-01

A low-cost edge-AI smart floor mat using multi-point force sensors for real-time fall detection and elderly safety

10.11591/ijres.v15.i2.pp350-363
Sahapong Somwong , Chatree Homkhiew , Thanwit Naemsai , Athirot Mano
This study describes the creation of a smart floor mat (SFM) that integrates edge-based artificial intelligence (AI) processing on an embedded system to identify movements such as standing, sitting, and falling to improve the safety of the elderly. The design incorporates nine force sensitive resistor (FSR) sensors, an ESP32 microcontroller, and a multi-class support vector machine (SVM) algorithm to analyze the sensor data in real time or long-time immobility detection, the device will automatically switch on and activate alarms to alert tele-caregivers and helpers via Telegram Bot notifications, indicator lights, and speakers for immediate responses. Experimental results demonstrated that the classification accuracy was 93.33% in model evaluation and 88.33% on the embedded platform, respectively, with an F1-score of 0.82-0.83 and an utterly perfect fall event detection (100%). Data are automatically logged in Google Sheets through Wi-Fi for trend analysis and health monitoring. The proposed SFM is low-cost, foldable, portable, and capable of supporting real-time monitoring and proactive safety management in the elderly. This innovation contributes to the development of smart home healthcare systems and is in line with the goal of achieving a better quality of life.
Volume: 15
Issue: 2
Page: 350-363
Publish at: 2026-07-01

An IoT-enabled vision-aid for the blind integrating ultrasonic obstacle detection and GPS-based location tracking

10.11591/ijres.v15.i2.pp386-395
Varuna Kumara , Akshatha Naik , Ashwini Ashwini , Navilgone Krishna Vaishnavi , Ruchitha Kamath Subhashchandra , Trapthi Trapthi
Visual impairment significantly affects independent mobility and personal safety, creating a need for affordable and reliable assistive navigation technologies. This paper presents the design and implementation of a low-cost wearable Vision-Aid system to support visually impaired individuals during outdoor navigation. The primary objective of the study is to enhance obstacle awareness, location tracking, and emergency communication using accessible embedded technologies. The proposed system integrates ultrasonic sensors for real-time obstacle detection, an Arduino microcontroller for data processing, a global positioning system (GPS) module for location tracking, and a global system for mobile communication (GSM) module for emergency alert transmission. Audio feedback is provided through a voice module to guide the user safely. Experimental evaluations were conducted under various environmental conditions to assess obstacle detection accuracy, response time, and location reliability. The results demonstrate accurate obstacle detection, timely audio alerts, and reliable real-time location sharing with caregivers. The proposed system improves user confidence, mobility, and safety while maintaining low implementation cost. This work highlights the potential of embedded and internet of things (IoT)–based assistive devices to enhance autonomy for visually impaired individuals and provides a foundation for future integration of artificial intelligence (AI)-based object recognition.
Volume: 15
Issue: 2
Page: 386-395
Publish at: 2026-07-01

ESP-NOW based multi-node internet of things system for agricultural solar dryers with hybrid offline-online monitoring

10.11591/ijres.v15.i2.pp426-438
Rahmat Siswanto , Putri Dewintari , Sapar Sapar
Existing internet of things (IoT) systems for agricultural solar dryers rely on continuous internet connectivity, limiting deployment in remote rural areas with unreliable infrastructure. This study develops and validates a multi-node IoT architecture using ESP-NOW peer-to-peer communication that enables infrastructure-independent operation with optional ThingSpeak cloud synchronization. The system integrates ESP32 nodes, SHT41 sensors, relay-controlled actuators, and a hybrid solar-battery-grid power supply, deployed at a cocoa processing facility in South Sulawesi, Indonesia. Field evaluation confirmed: >95% packet delivery at 50 m, <10 ms latency, 94.3% cloud synchronization reliability, and 96% upload timing precision within ±2 s (σ=0.96 s). The dual-mode architecture sustained continuous local monitoring and actuator control during all network outages, with autonomous cloud reconnection requiring no manual intervention. Drying trials showed a -40 57% reduction in cocoa drying duration (3–4 days vs. 5–7 days baseline) through automated chamber control (40–60 °C; RH <60%). Energy analysis yielded an intensity of 0.12–0.17 kWh/kg dried output, with the IoT subsystem consuming less than 1% of total drying energy. The validated architecture provides a deployable, offline-capable, and energy-efficient solution for post-harvest monitoring in infrastructure-constrained environments, with applicability to diverse crop drying and storage scenarios.
Volume: 15
Issue: 2
Page: 426-438
Publish at: 2026-07-01

A dynamic geofencing and dwell-time validation system for secure attendance tracking in higher education: methodological proposal

10.11591/csit.v7i2.p159-166
Michael Favour Edafeajiroke , Amanda Eromosele Ekata
Accurate attendance tracking is vital for student engagement and academic integrity, yet traditional methods are prone to error and proxy attendance. While technological solutions like biometrics and QR codes exist, they often suffer from high costs, privacy concerns, and an inability to verify continuous presence. This study proposes a dynamic geolocation-based attendance system to address these gaps. Developed with Flutter and Node.js, the system employs lecturer-defined geofences and a dwell-time validation rule, confirming attendance only if a student remains within the designated area for at least 80% of the class duration. It features cross-platform accessibility, role-based dashboards, real-time notifications, and exportable reports. The methodology followed an Agile approach, focusing on user-centered design and robust backend development. The resulting system offers a cost-effective, scalable solution that enhances accuracy, prevents proxy attendance, and supports the digital transformation of higher education administration.
Volume: 7
Issue: 2
Page: 159-166
Publish at: 2026-07-01

Matter protocol-enabled device onboarding for cross-platform internet of things systems

10.11591/ijres.v15.i2.pp406-415
Geetishree Mishra , Hemavathi Hemavathi , Harish V Mekali
The Matter protocol, created by the connectivity standards alliance (CSA), comes in with a single standard to make sure these devices can connect and be controlled across platforms like Google Home, Apple HomeKit, Amazon Alexa, and Samsung SmartThings. The rapid expansion of the internet of things (IoT) is driving the urgent need for secure and efficient onboarding processes for a wide range of connected devices. It necessitates a robust framework to seamlessly integrate new additions into existing systems while upholding security standards. This initiative focuses on implementing the Matter protocol on ESP32 devices, employing a Raspberry Pi hub as the central communication point to facilitate smooth device-to-hub interactions. This work presents the onboarding devices for interconnected IoT systems using the Matter protocol. The Matter device is configured and tested within the Amazon ecosystem using an Alexa Echo Dot, as well as with the smart home assistant ecosystem along with a smartphone application. By configuring the Raspberry Pi hub as a designated Matter hub and exploring interactions within the home assistant ecosystem supporting diverse platforms like Apple HomeKit and Google Home, the work enhanced interoperability and broadened the utility of IoT devices within an interconnected network. This initiative forges a foundation for an adaptable and cohesive IoT environment.
Volume: 15
Issue: 2
Page: 406-415
Publish at: 2026-07-01

Implementation and design of GPS tracker monitoring system on car rental vehicles based on internet of things using Nodemcu ESP-32

10.11591/csit.v7i2.p214-223
Indah Purnama Sari , Al-Khowarizmi Al-Khowarizmi , Asrar Aspia Manurung
Internet of things (IoT) based vehicle tracking system is an effective solution to overcome various problems in the vehicle rental industry, such as asset loss, route misuse, and late returns. This study aims to design and implement a real-time vehicle position monitoring system using the NodeMCU ESP-32 module integrated with the NEO-6M GPS module and Wi-Fi connectivity to send data to a cloud-based server. This system is designed to display the vehicle position directly through a web-based digital map interface, which can be accessed by vehicle owners anytime and anywhere. The methodology used includes hardware and software design, location accuracy testing, and data integration with a web-based visualization platform using a map API. The test results show that the system is capable of sending vehicle location data with a position accuracy level of up to ±5 meters and data updates every 10 seconds under stable network conditions. In addition, the system has good power efficiency, with an average current consumption of 80–100 mA when active. All data was successfully stored and visualized in real-time using the Google Maps API, and the system was able to operate stably for 24 hours of non-stop testing. Based on these results, the IoT-based GPS tracker system with NodeMCU ESP-32 can be effectively implemented on rental vehicles as a modern monitoring solution that is cost-effective, flexible, and easily accessible. This system provides added value in fleet monitoring and supports faster and data-based decision making.
Volume: 7
Issue: 2
Page: 214-223
Publish at: 2026-07-01

Binary hybrid pathfinder algorithm for efficient feature selection in resource-constrained embedded systems

10.11591/ijres.v15.i2.pp504-513
Rahul Mirajkar , Premanand Ghadekar , Vijay Dasharath Chougule , Renuka Bhandari , Hridaynath Khandagale , Mahavir A. Devmane , Mangesh Hajare , Kuldeep B. Vayadande
Feature selection is critical for embedded machine learning systems where computational resources and memory are severely constrained. This paper presents the binary quadratically interpolated hybrid pathfinder algorithm (BQIHPFA), a novel metaheuristic optimization method designed for efficient feature subset selection in resource-limited classification tasks. BQIHPFA adapts the continuous QIHPFA to binary search spaces through sigmoid transfer functions and employs a hybrid two-group enhancement strategy combining pathfinder dynamics with salp swarm algorithm-inspired exploration. We evaluate BQIHPFA against three established binary optimization algorithms (binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization (BWO)) on three benchmark datasets with varying dimensionalities: Língua Brasileira de Sinais (Brazilian Sign Language) movement (90 features), Parkinson's disease detection (22 features), and Sonar Rock vs. Mine (60 features). Experimental results demonstrate that BQIHPFA achieves competitive classification accuracy (average 83.57%) with substantial feature reduction (average 64.1%) while executing 5.2 times faster than complex baselines and consuming minimal memory (peak: 45-58 MB). Ablation experiments demonstrate that every algorithmic part makes a 8-24% contribution to the total performance. BQIHPFA offers an easy-to-use, non-specific feature selection method to automated resource-constrained embedded classification systems, applicable to be deployed to low-power computing environments, and internet of things (IoT) edge systems.
Volume: 15
Issue: 2
Page: 504-513
Publish at: 2026-07-01

Performance evaluation of the deep learning system for weed recognization

10.11591/csit.v7i2.p167-178
Abd Abrahim Mosslah , Reyadh Hazim Mahdi , Hassan Kassim Albahadily
Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilization of deep neural networks (DNNs) to differentiate between various weed types in actual events scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design the robust deep learning system. To simplify such process and conserve resources, researchers have explored a fresh method known as automated deep learning our technology’s recognization of weeds through the use of machine learning was evaluated using plant seedlings and weed collections from plants dataset to address a issue of weed recognization. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training with datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align together with automated machine learning (AutoML-linked) studies, while fall short of manually fine-tuned deep-learning-based systems created through human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.
Volume: 7
Issue: 2
Page: 167-178
Publish at: 2026-07-01

Improving Botnet host prediction with encryption and GRU for enhanced network security

10.11591/csit.v7i2.p141-158
Omega Joel Patria Moata , Irwansyah Saputra
This paper examines the challenges of reliably and securely predicting Botnet hosts, a crucial aspect of network security. Existing Botnet detection systems often fail to address data privacy concerns and struggle with evolving attack methods. This study proposes an innovative approach to improve the security and accuracy of Botnet host prediction by integrating deep learning with encryption. The proposed method employs encryption techniques such as data encryption standard (DES) and blum-blum-shub (BBS) to protect sensitive data in a text data set of 2,100 IP addresses, consisting of Botnet hosts and benign hosts. Several pre-processing techniques, including moving average and missing value handling, are implemented to optimize the model performance. The effectiveness of the system is evaluated using performance metrics such as F1-score, recall, accuracy, and precision. Experimental results show that the proposed approach significantly outperforms existing methods in accuracy, which have not achieved the maximum accuracy per IP Host within a given time frame, while providing enhanced security through encryption on text data. The study concludes that combining deep learning with encryption on text data offers a promising solution for reliable and secure Botnet host prediction data. Future research will focus on testing larger and more diverse data sets, as well as analyzing the impact of different encryption techniques on the overall accuracy and security of the system.
Volume: 7
Issue: 2
Page: 141-158
Publish at: 2026-07-01

A real-time multi-modal deep learning framework for student attentiveness assessment in online learning environments

10.11591/ijres.v15.i2.pp450-460
Rajasekaran Mariswamy , P.V. Praveen Sundar
The rapid growth of online learning platforms has increased the need for intelligent systems capable of monitoring student attentiveness in real time to improve learning effectiveness and adaptive instruction. This paper proposes a multi-modal deep learning framework for attentiveness assessment by integrating visual, behavioral, and temporal information extracted from online classroom interactions. The proposed system consists of four major components, namely data acquisition, preprocessing and normalization, deep feature extraction with temporal learning, and attentiveness evaluation with analytics generation. Visual and spatial characteristics are learned using a convolutional neural network (CNN), while temporal behavioral patterns are captured through a long short-term memory (LSTM) network to model sequential engagement dynamics. The framework is designed to operate in both real-time and offline modes, enabling live monitoring during virtual classes as well as post-session analysis of recorded lectures. The computational pipeline is optimized through fixed-point processing, parallel convolution execution, and latency-aware temporal modeling, making it suitable for field programmable gate array (FPGA)-based and embedded implementations under constrained computational resources. Experimental evaluation conducted on an in-house dataset demonstrates that the proposed framework achieves 92.9% classification accuracy and a 91.9% F1-score, while maintaining strong generalization capability on cross-dataset benchmarks. Furthermore, latency analysis shows an average processing time of 31.6 ms per frame, enabling near real-time inference at approximately 30 frames per second.
Volume: 15
Issue: 2
Page: 450-460
Publish at: 2026-07-01

High-performance approximate MAC multiplier using majority logic compressors for CNNs

10.11591/ijres.v15.i2.pp269-280
Selvarasan Radhakrishnan , Sudhagar Govindhaswamy , Rasadurai Kumaravel
This research presents an optimized multiple accumulate (MAC) unit multiplier design for efficient convolutional neural network (CNN) operations. This design mainly focuses on making the multiplier systems smaller by using approximate majority compressor methods instead of the usual and traditional approximate methods. The traditional approximate multiplier compressor techniques are leads to increases in logic size, critical path delay, and power consumption; however, the proposed research mitigates these problems and solves them with a novelty-based approach in the Dadda multiplier technique. The novelty of this approach is to reduce the number of stages in the multiplier design using 4:2, 5:2, and 7:2 compressors. This compressor is designed with an approximate method using majority logic; compared to this traditional method, the proposed majority approximate compressor method processed less error differences in multiplication output. The proposed approaches resulted in significant reductions in area, power, and delay relative to traditional multipliers. This research compared seven unique comparisons of MAC-based multiplier architecture, and it will have been developed in Verilog hardware description language (HDL) and synthesized on the Xilinx Vertex-5 FPGA, providing reductions of 58.4% in lookup table (LUT) and 76.2% in occupied slices, and proving less power consumption. This design is a highly suitable approach for real-time CNN and digital signal processing (DSP) applications.
Volume: 15
Issue: 2
Page: 269-280
Publish at: 2026-07-01

Using OOA-based proportional-integral-derivative controller to enhance the charging and discharging of battery voltage

10.11591/ijres.v15.i2.pp364-372
Hassanin Falah Abdul Hassan , Issa Ahmed Abed
Today, hybrid energy harvesters are critical in promoting technological advancement by generating sustainable energy and addressing the financial and environmental concerns around batteries. Because of their unexpected input behavior, hybrid energy harvesters present a challenge in producing the necessary stable energy. Thus, this study provides a power conditioning circuit with an optimal controller. Three proportional-integral-derivative (PID) controllers control the charging and discharging of the battery's bidirectional converter. To improve system performance actively and optimally, optimization algorithms are implemented for the optimization of the PID parameters. Osprey optimization algorithm (OOA)-based PID is used, and its performance is compared with five optimization algorithims (Chimp optimization algorithm (ChOA)-based PID, hony badger algorithm (HBA)-based PID, Zebra optimization algorithm (ZOA)-based PID, and cheetah optimization algorithm (COA)-based PID. The comparison between algorithms was done based on the minimum fitness function value, which shows that the OOA is the best one. All results are implemented in MATLAB/Simulink using the 2021a version as follows: (ChOA 3.061%, CO 4.737%, HBA 3.03%, ZOA 3.058%, and OOA 1.52%).
Volume: 15
Issue: 2
Page: 364-372
Publish at: 2026-07-01

Optimizing water distribution in Harare, Zimbabwe using IoT and cloud computing

10.11591/csit.v7i2.p231-240
Angeline Tsatsa , Tinashe Butsa , Yolanda Chibaya
Rapid urbanization in Harare, Zimbabwe, has intensified inefficiencies in water distribution, resulting in high non-revenue water (NRW) and inequitable supply. This paper presents a novel data-driven framework that integrates internet of things (IoT) sensors, machine learning (ML), and cloud computing to optimize urban water distribution. Historical and real-time data including water flow, pressure, and consumption are collected via IoT sensors and analyzed using a random forest model for accurate demand forecasting and anomaly detection, such as leaks. The model is deployed on a secure cloud-based ASP.NET platform, enabling real-time monitoring and automated valve control through ultrasonic sensors over Wi-Fi. Evaluation demonstrates superior performance with R²=0.89 for demand forecasting and anomaly detection metrics of 94% accuracy, 91% precision, 92% recall, and 91% F1-score, outperforming baseline methods. This integrated system reduces water loss, improves supply equity, and provides a scalable and cost-effective approach for smart water management in resource-constrained urban settings. The framework offers practical insights for policymakers and utilities seeking to implement sustainable, technology-driven water management solutions in developing cities.
Volume: 7
Issue: 2
Page: 231-240
Publish at: 2026-07-01

Fuzzy logic–based consensus protocol for educational blockchain networks

10.11591/csit.v7i2.p131-140
Igor Ivanov , Svetlana Zhdanova
This paper addresses the growing challenge of ensuring trust, authenticity, and transparency in the management and verification of educational credentials within modern, digitally oriented learning ecosystems. Rapid expansion of e-learning, lifelong learning, and global mobility has intensified document fraud, revealing the limitations of traditional verification mechanisms. To respond to these systemic risks, the study proposes a socially oriented block-validation protocol integrated into a distributed blockchain environment designed specifically for educational data security. The protocol forms the core of the EduBLOCK system, developed by the authors, and introduces an innovative consensus mechanism that incorporates human-centered reputation assessments rather than computational or financial power. The approach employs fuzzy-set theory to evaluate user activity, institutional credibility, and delegate reputation, enabling a more nuanced and context-sensitive model of trust. Delegates responsible for validating blocks are selected through a dynamic, reputation-driven procedure that excludes financial contributions and subjective parameter tuning. The proposed algorithm combines cryptographic guarantees, peer-to-peer (P2P) communication, and soft-computing methods to ensure fairness, prevent manipulation, and maintain stable system functioning. Block validity is determined through open voting, requiring approval by more than two-thirds of elected delegates.
Volume: 7
Issue: 2
Page: 131-140
Publish at: 2026-07-01
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